STUDY OF HUMAN THERMOREGULATION: ADAPTIVE OPTIMIZATION CONTROL THEORY ANALYSIS

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Abstract
An example of homeostasis is temperature regulation at a desired level; this physiological process leads to the preservation of a stable biological environment. A control-theory–based model permits a biomedical engineer to understand the complex operation of thermoregulation, by converting general information to knowledge, and can be integrated to see how systemic parameters influence the entire system. The thermal inputs organized in the hypothalamus to activate thermoregulation responses to heat and cold stimuli, with the widely accepted set-point hypothesis for the regulation of body temperature from a control systems point of view, are, however, not entirely known. There are circumstances (e.g. fever) in which the presumed set-point mechanism appears to break down. This paper evaluates a novel set-level adaptive optimal thermal control paradigm inspired by Hebbian covariance synaptic adaptation, previously proposed based on its potential to predict the homeostatic respiratory system. It introduces a Hebbian feedback covariance learning (HFCL) concept in order to align a neuronal network into the analysis of the thermoregulation system. Hebbian theory is concerned with how neurons connect among themselves to become engrams. The passive-active mathematical model for simulating human thermoregulation during exercise was compared in cool, warm, and hot environments, and then was translated into MATLAB to predict thermoregulation. The two-node core and shell model predictions are comparable with observed thermoregulation responses from the existing literature. The thermoregulation changes with respect to proportionality constant and sensitivity of the receptors. A reasonably general agreement with the measured mean group data of earlier performed laboratory exercise studies was obtained for peak temperature, although it tended to overpredict the core body temperature.

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